CN114126913A

CN114126913A - Electric vehicle charging station reliability assessment method and device

Info

Publication number: CN114126913A
Application number: CN202080052286.1A
Authority: CN
Inventors: J·阿蒂卡里
Original assignee: Vita Ltd
Current assignee: Vita Ltd
Priority date: 2019-11-04
Filing date: 2020-11-03
Publication date: 2022-03-01
Anticipated expiration: 2040-11-03
Also published as: JP7565953B2; EP3959100A1; US12162372B2; FI128774B; DK3959100T3; FI20195944A1; JP2023500424A; EP3959100B1; US20220355696A1; WO2021089914A1; CN114126913B

Abstract

The purpose is to provide a charging station reliability assessment device and a charging station reliability assessment method. According to an embodiment, a method includes obtaining historical information of an electric vehicle EV charging station. The first reliability index and the second reliability index may be calculated based on historical information. Based on the first reliability index and the second reliability index, a third reliability index may be calculated. A method, apparatus, and computer program product are provided.

Description

Electric vehicle charging station reliability assessment method and device

Technical Field

The present disclosure relates to electric vehicle charging, and more particularly, to electric vehicle charging station reliability assessment methods and apparatus.

Background

Various types of problems may arise with Electric Vehicle (EV) charging stations. Sometimes, the charging station may go offline, sometimes the charging station sends various error codes, and sometimes the charging station may malfunction without a clear indication of the underlying cause. This is problematic from the standpoint that the customer wishes to charge their EV: customers do not know whether the EV charging stations they will use are operating properly. In particular, if customers are traveling over long distances, the customers need reliable information about the condition of the charging stations they are planning to use.

In a more challenging scenario, EV charging stations may be online and communicate with a management backend system. The EV charging station may not even send any error messages to the backend system. However, it is quite common that EV charging stations will not function properly even if these types of indicators do not show any signs of problems. Therefore, assessing reliability of EV charging stations can be challenging.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The purpose is to provide a charging station reliability evaluation device and a charging station reliability evaluation method. The foregoing and other objects are achieved by the features of the independent claims. Further forms of realization are apparent from the dependent claims, the description and the drawings.

According to a first aspect, a method comprises: obtaining historical information of an electric vehicle EV charging station; calculating a first reliability index (reliability index) based on the historical information, wherein the first reliability index corresponds to a reliability of the EV charging station during the first time period; calculating a second reliability index based on the historical information, wherein the second reliability index corresponds to reliability of the EV charging station during a second time period; and calculating a third reliability index based on the first reliability index and the second reliability index. The method may, for example, enable reliability assessment of EV charging stations.

In an implementation form of the first aspect, the length of the first time period is shorter than the length of the second time period. The method may, for example, enable reliability assessment of the EV charging station by assessing the short-term reliability of the EV charging station using a first reliability index, and assessing the long-term reliability of the EV charging station using a second reliability index. These may then be combined into a third reliability index in order to evaluate the overall reliability of the EV charging station.

In another implementation form of the first aspect, the first time period corresponds to a time period between a current time and a first time threshold. The method may implement a reliability assessment of the EV charging stations during the recent history, for example, by using the first reliability index.

In another implementation form of the first aspect, the second time period corresponds to a time period between the first time threshold and the second time threshold. The method may implement a reliability assessment of the EV charging station during the next recent history, for example, by using the second reliability index.

In another implementation form of the first aspect, the length of the first period of time is in a range of 12 hours to 10 days. The method may implement a reliability assessment of the EV charging stations during the recent history, for example, by using the first reliability index.

In another implementation form of the first aspect, the length of the second time period is in a range of 30 days to 12 months. The method may implement a reliability assessment of the EV charging station during the next recent history, for example, by using the second reliability index.

In another implementation form of the first aspect, the step of calculating the first/second reliability index based on the historical information comprises: assigning a score (points) to at least one reliability indicator (reliability indicator) based on the historical information; and calculating a first/second reliability index based on the assigned score. The method may enable reliability assessment of an EV charging station, for example, by considering various factors.

In another implementation form of the first aspect, the at least one reliability indicator comprises at least one of: offline time percentage of EV charging stations; percentage of time to failure of EV charging stations; the number of warnings sent by the EV charging station; a number of charging performed in a time shorter than a preconfigured minimum time threshold using an EV charging station; a number of charging performed using an EV charging station for longer than a preconfigured maximum time threshold; a number of charging performed with charging energy smaller than a preconfigured energy threshold using an EV charging station; the number of failed charges performed using the EV charging station; or the number of successful charging performed using an EV charging station. The method may implement reliability assessment of EV charging stations, for example, by considering various factors that may reflect the reliability of the EV charging stations.

It is to be understood that the implementations of the first aspect described above may be used in combination with each other. Several implementations may be combined together to form another implementation.

According to a second aspect, there is provided a computer program product comprising program code configured to perform the method according to the first aspect when the computer program is executed on a computer.

According to a third aspect, a computing device is configured to: obtaining historical information of an electric vehicle EV charging station; calculating a first reliability index based on the historical information, wherein the first reliability index corresponds to reliability of the EV charging station during a first time period; calculating a second reliability index based on the historical information, wherein the second reliability index corresponds to reliability of the EV charging station during a second time period; and calculating a third reliability index based on the first reliability index and the second reliability index. With such a configuration, the computing device may, for example, evaluate the reliability of the EV charging station.

In an implementation form of the third aspect, the length of the first time period is shorter than the length of the second time period. With such a configuration, the computing device may evaluate the reliability of the EV charging station, for example, by evaluating the short-term reliability of the EV charging station using the first reliability index, and by evaluating the long-term reliability of the EV charging station using the second reliability index. These may then be combined into a third reliability index in order to evaluate the overall reliability of the EV charging station.

In another implementation form of the third aspect, the length of the first period of time is in a range of 12 hours to 10 days. The method may implement a reliability assessment of the EV charging stations during the recent history, for example, by using the first reliability index.

In another implementation form of the third aspect, the length of the second time period is in a range of 30 days to 12 months. With such a configuration, the computing device can evaluate the reliability of the EV charging station during the next recent history, for example, by using the second reliability index.

In another implementation form of the third aspect, the computing device is configured to compute the first/second reliability index based on the historical information by performing the following operations: assigning a score to at least one reliability indicator based on historical information; and calculating a first/second reliability index based on the assigned score. With such a configuration, the computing device may evaluate the reliability of the EV charging station, for example, by considering various factors.

In another implementation form of the third aspect, the at least one reliability indicator includes at least one of: offline time percentage of EV charging stations; percentage of time to failure of EV charging stations; the number of warnings sent by the EV charging station; a number of charging performed in a time shorter than a preconfigured minimum time threshold using an EV charging station; a number of charging performed using an EV charging station for longer than a preconfigured maximum time threshold; a number of charging performed with charging energy smaller than a preconfigured energy threshold using an EV charging station; the number of failed charges performed using the EV charging station; or the number of successful charging performed using an EV charging station. With such a configuration, the computing device may evaluate the reliability of the EV charging station, for example, by considering various factors that may reflect the reliability of the EV charging station.

In another implementation form of the third aspect, the first time period corresponds to a time period between a current time and a first time threshold. The computing device is able to evaluate the reliability of the EV charging station during the recent history, for example, by using the first reliability index.

In another implementation form of the third aspect, the second time period corresponds to a time period between the first time threshold and the second time threshold. The computing device can, for example, evaluate the reliability of the EV charging station during the next recent history by using the second reliability index.

It is to be understood that the implementations of the third aspect described above may be used in combination with each other. Several implementations may be combined together to form another implementation.

Many of the attendant features will be more readily appreciated as the same becomes better understood by reference to the following detailed description considered in connection with the accompanying drawings.

Drawings

Example embodiments are described in more detail below, with reference to the attached drawing figures, wherein:

fig. 1 illustrates a flowchart representation of a method for charging station reliability assessment according to an embodiment;

FIG. 2 illustrates a schematic representation of a computing device for electric vehicle charging station reliability evaluation, according to an embodiment;

FIG. 3 illustrates a schematic representation of an electric vehicle charging system according to an embodiment;

FIG. 4 illustrates a schematic representation of a timeline in accordance with an embodiment; and

fig. 5 illustrates a schematic representation of data used for electric vehicle charging station reliability evaluation, according to an embodiment.

In the following, the same reference numerals are used to designate the same parts in the drawings.

Detailed Description

In the following description, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific aspects in which the disclosure may be practiced. It is to be understood that other aspects may be utilized and structural or logical changes may be made without departing from the scope of the present disclosure. The following detailed description, therefore, is not to be taken in a limiting sense, as the scope of the present disclosure is defined by the appended claims.

For example, it is to be understood that the disclosure relating to the described method may also apply to a corresponding apparatus or system configured to perform the method, and vice versa. For example, if a specific method step is described, the corresponding apparatus may comprise means for performing the described method step, even if such means are not explicitly described or illustrated in the figures. On the other hand, for example, if a particular device is described based on functional units, the corresponding method may include steps to perform the described functions, even if such steps are not explicitly described or illustrated in the figures. Further, it is to be understood that features of the various example aspects described herein may be combined with each other, unless specifically noted otherwise.

Fig. 1 illustrates a flowchart representation of a method 100 for Electric Vehicle (EV) charging station reliability assessment, according to an embodiment.

According to an embodiment, the method 100 comprises the steps of: historical information of an electric vehicle EV charging station is obtained 101.

The historical information may include any information related to the use of EV charging stations. The history information may also be referred to as history, log information, or the like. The history information may include, for example, a log of various charging events for the EV charging station. The log may further include: the length of each charging session, the amount of energy charged during the charging session, information about failed charging sessions, messages sent by the EV charging stations (such as error messages), any errors that occurred, and/or the period of time during which the EV charging stations failed.

The obtaining step 101 may be performed by a computing device coupled to the EV charging station, for example, via a telecommunications network/link. Such a computing device may collect historical information by, for example, communicating with multiple EV charging stations. Each EV charging station may include a computing device that may be configured to collect data, such as usage data, about the EV charging station.

An EV charging station may refer to a device that may be used to charge an EV (such as an electric vehicle). An EV charging network may refer to a network of EV charging stations. Each EV charging station in the EV charging network may be connected to a computing device, such as a server, for example, via a telecommunications network or similar network. For example, a computing device may be used to monitor and/or manage EV charging stations in an EV charging network.

The method 100 may further include the steps of: a first reliability index is calculated 102 based on the historical information, wherein the first reliability index corresponds to reliability of the EV charging station during a first time period.

The first reliability index may also be referred to as a first reliability value or the like. The first reliability index may quantify a degree of reliability of the EV charging station during the first time period. A larger value may indicate that the EV charging station is more reliable, while a smaller value may indicate that the EV charging station is less reliable, or vice versa.

The first time period may also be referred to as a first period of time, a first time interval, or the like. The first time period may correspond to any length of time period.

The first reliability index may be calculated, for example, by selecting events from the historical information that have occurred during the first time period. For example, the first subset may be selected from historical information. The first subset may correspond to a first time interval. A first reliability index may be calculated based on the first subset.

The method may further comprise the steps of: a second reliability index is calculated 103 based on the historical information, wherein the second reliability index corresponds to the reliability of the EV charging station during a second time period.

The second reliability index may also be referred to as a second reliability value or the like. The second reliability index may quantify a degree of reliability of the EV charging station during the second time period. A larger value may indicate that the EV charging station is more reliable, while a smaller value may indicate that the EV charging station is less reliable, or vice versa.

The second reliability index may be calculated, for example, by selecting from the historical information, events that have occurred during the second time period. For example, the second subset may be selected from historical information. The second subset may correspond to a second time interval. A second reliability index may be calculated based on the second subset.

The second time period may also be referred to as a second time period, a second time interval, or the like. The second time period may correspond to any length of time period. The second time period may be a different time period than the first time period. The length of the second period of time may be greater than the length of the first period of time.

The step of calculating 102 the first reliability index and the step of calculating 103 the second reliability index may be performed in any order or substantially simultaneously.

The method 100 may further include the steps of: a third reliability index is calculated 104 based on the first reliability index and the second reliability index.

The third reliability index may also be referred to as a third reliability value, a total reliability index, a total reliability value, a reliability index, or the like. The third reliability index may quantify how reliable the EV charging station is during the first time period and the second time period.

For example, the third reliability index may be calculated as an average of the first reliability index and the second reliability index. The average may be, for example, an arithmetic average, a geometric average, a weighted average, a quadratic average, or the like. For example, if the first reliability index corresponds to a more recent reliability of the EV charging station, it may be beneficial to weight the first reliability index more when calculating the third reliability index, so that the third reliability index more reflects the current condition of the EV charging station.

The first/second reliability index may combine a number of different information sources and indirect indicators of possible problems. These may then be combined into a single index, a third reliability index, which may indicate to the user how well they can believe that the station they are planning to use will function properly.

The first reliability index may correspond to a current state of the EV charging station. The second reliability index may correspond to a reliability history of the EV charging station. The length of the first period of time may be less than the length of the second period of time.

According to an embodiment, the length of the first period of time is in the range of 12 hours to 10 days. The length of the first time period may be within any subrange of this range, such as 12 hours to 48 hours, 12 hours to 5 days, or 24 hours to 4 days.

According to an embodiment, the length of the second period of time is in the range of 30 days to 12 months. The length of the second time period may be within any subrange of this range, such as 30 days to 90 days, 60 days to 120 days, or 30 days to 6 months.

The current state may indicate how well the EV charging station was operating during, for example, the last 24 hours. This may be useful primarily because even though an EV charging station may have worked well in the past, it may have begun to fail today and thus customers cannot trust that the EV charging station will be operating properly today. Or on the other hand, EV charging stations may have many problems in history, but these problems may have been repaired and the station may be operating normally today.

The reliability history may indicate how well the EV charging station was operating within, for example, the last 4 months. This can be a useful reliability indicator for customers to know if they can believe that the station is functioning properly. Even if there is not any problem today, if the reliability history is poor, it is likely that the problem will occur again today.

Fig. 2 illustrates a schematic representation of a computing device 200 according to an embodiment.

The computing device 200 may include at least one processor 201. The at least one processor 201 may include, for example, one or more of various processing devices such as a coprocessor, a microprocessor, a controller, a Digital Signal Processor (DSP), processing circuitry with or without an accompanying DSP, or various other processing devices including integrated circuits such as, for example, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like.

The computing device 200 may also include memory 202. The memory 202 may be configured, for example, to store a computer program or the like. The memory 202 may include one or more volatile memory devices, one or more non-volatile memory devices, and/or a combination of one or more volatile and non-volatile memory devices. For example, the memory 202 may be embodied as a magnetic storage device (such as a hard disk drive, a floppy disk, a magnetic tape, etc.), an optical magnetic storage device, and a semiconductor memory (such as a mask ROM, a PROM (programmable ROM), an EPROM (erasable PROM), a flash ROM, a RAM (random access memory), etc.).

When the computing device 200 is configured to implement a certain functionality, certain components and/or certain components of the computing device 200 (such as the at least one processor 201 and/or memory 202) may be configured to implement that functionality. Also, when the at least one processor 201 is configured to implement a certain functionality, the functionality may be implemented using, for example, program code included in the memory 202. For example, if the computing device 200 is configured to perform an operation, the at least one memory 202 and the computer program code may be configured, with the at least one processor 201, to cause the computing device 200 to perform the operation.

According to an embodiment, the computing device 200 is configured to obtain historical information of an electric vehicle EV charging station.

The computing device 200 may also be configured to calculate a first reliability index based on the historical information, wherein the first reliability index corresponds to reliability of the EV charging station during the first time period.

The computing device 200 may also be configured to calculate a second reliability index based on the historical information, wherein the second reliability index corresponds to reliability of the EV charging station during the second time period.

The computing device 200 may be configured to perform the computing of the first and second reliability indices in any order or substantially simultaneously.

The computing device 200 may be further configured to calculate a third reliability index based on the first reliability index and the second reliability index.

Fig. 3 illustrates a schematic representation of a system 300 for EV charging station reliability assessment according to an embodiment.

The system 300 may include: EV charging network 301, computing device 200, and/or user 303. The EV charging network 301 may include a plurality of EV charging stations 302.

Computing device 200 may communicate with EV charging network 301, for example, using a data connection. The data connection may be any connection that enables computing device 200 to communicate with EV charging network 301. The data connection may include, for example: the internet, ethernet, 3G, 4G, Long Term Evolution (LTE), New Radio (NR), Wi-Fi, or any other wired or wireless connection, or some combination of these. For example, the data connection may include a wireless connection such as Wi-Fi, an internet connection, and an ethernet connection.

The computing device 200 may be implemented as part of a backend system. The backend system may include, for example, a plurality of servers, and the computing device 200 may correspond to one or more of those servers. The backend system may be configured to monitor and/or manage EV charging network 301 and/or EV charging station 302.

The computing device 200 may be configured to obtain the history information from the EV charging network 301. The computing device may be configured to obtain historical information from one or more EV charging stations 302.

The user 303 may interact with the computing device 200. The interaction may be a direct interaction via, for example, a user interface, or an indirect interaction. The user 303 may be an end customer, for example. The user 303 may send a query to the computing device 200 using a mobile device, such as a mobile phone, for example. In the query, the user 303 may request a reliability index for one or more EV charging stations. The computing device 200 may calculate the reliability index in response to the query, or the computing device 200 may calculate the reliability index in advance. The computing device 200 may then send the requested reliability index to the user 303. After receiving the reliability index, the user 303 may select which EV charging station they will use.

For the end-customer, a typical communication means for reliability is a mobile application or a web-based application, where the user 303 can view the status of different EV charging stations. When the user 303 opens information about EV charging stations from the mobile app, there may be a simple traffic light view of the reliability of the EV charging stations. For example, if the reliability index is within the range of 0 to 100, red may indicate 0 to 33 points, yellow may indicate 34 to 66 points, and green may indicate 67 to 100 points. This may indicate the reliability index of the EV charging station to the user 303 in a simplified format.

Alternatively, the user 303 may be a charging station owner/administrator. Computing device 200 may send more comprehensive reports to such users. Such reports may be sent automatically by email or seen in a web-based management portal, for example. The EV charging station owner report may show a summary of all EV charging stations of the owner, for example using the aforementioned traffic light representation and/or per-station details. For example, the report may indicate the lowest scoring factor in the historical information. This more detailed information can then be used by the user 303 to see which of their EV charging stations are not operating well and which EV charging stations may need maintenance or replacement.

The method 100 may further include the steps of: the third reliability index is provided to the user 303. The providing step may include, for example: the third reliability index is sent to the user 303.

The computing device 200 may also be configured to provide a third reliability index to the user 303. The computing device 200 may be configured to send the third reliability index to the user 303, for example.

Alternatively or additionally, the computing device 200 may be configured to calculate the first/second/third reliability index without any input from the user 303. The computing device 200 may, for example, compile statistical data regarding the reliability indices for a plurality of EV charging stations 302. The administrator can then use this statistical data to infer, based on the reliability index, whether, for example, a particular model of EV charging station is not suitable for a particular application/condition.

FIG. 4 illustrates a schematic representation of a timeline in accordance with an embodiment. The embodiment of fig. 4 illustrates three moments: current time t ₀401. First time threshold t ₁402 and a second time threshold t ₂403. Current time t ₀401. First time threshold t ₁402 and a second time threshold t ₂403 may also be referred to as time of day. The embodiment of fig. 4 also illustrates two time periods: a first time period T ₁404 and a second time period T ₂405. The times 401 to 403 and the

time periods

404, 405 presented in the embodiment of fig. 4 are merely illustrative.

The first reliability index may correspond to a first time period T₁Reliability of the EV charging station during 404. The second reliability index may correspond to a second time period T₂Reliability of EV charging station during 405.

According to an embodiment, the first time period T ₁404 is shorter than the second period T ₂ 405。

Additionally or alternatively, the first time period T ₁404 and a second time period T ₂405 may be continuous. For example, when the second period T ₂405, a first time period T ₁404 may begin. In the embodiment of fig. 4, for example, the second period T ₂405 may be at a first time threshold t ₁402, and a first time period T ₁404 may be at a first time threshold t₁Beginning at 402.

Additionally or alternatively, the first time period T ₁404 may correspond to a current time t ₀401 and a first time threshold t₁The time period between 402. Current time t ₀401 may refer to a time when method 100 is performed and/or when computing device 200 performs the operations disclosed herein. As can be appreciated by those skilled in the art, although it may take some period of time to perform the operations and/or method 100, the length of such a period may be considered insignificant.

Additionally or alternatively, the second time period T₂ 405May correspond to a first time threshold t ₁402 and a second time threshold t₂The time period between 403. A first time period T₁The length of 404 may be within any subrange of this range, such as 12 hours to 48 hours, 12 hours to 5 days, or 24 hours to 4 days.

Additionally or alternatively, the second time period T ₂405 may be in the range of 30 days to 12 months in length. A second time period T ₂405 may be in any subrange of this range, such as 30 days to 90 days, 60 days to 120 days, or 30 days to 6 months in length.

Fig. 5 illustrates a schematic representation of data used for EV charging station reliability evaluation according to an embodiment.

Based on the historical information 501, various reliability indicators 502 may be assigned scores. The reliability indicators 502 may include, for example, one or more of the reliability indicators described herein.

Assigning a score to a reliability indicator may include, for example: a value of the reliability indicator is calculated and a score is assigned based on the value of the reliability indicator. For example, calculating the value of the reliability indicator may include: it is calculated based on historical information that a certain EV charging station is faulty within 10% of the first time interval 404. Then, based on the value of 10%, a certain numerical score may be assigned to the reliability indicator "percent time to failure". For example, the less time an EV charging station is faulty, the greater the value of the assigned score may be. Further examples of reliability indicators and scores assigned to the reliability indicators are disclosed herein.

The reliability indicator may be assigned a score for the first time interval 404 and the second time interval 405, respectively. For example, a score may be assigned to each reliability indicator of the first time interval 404 by selecting data corresponding to the first time interval 404 from the historical information 501. The data corresponding to the first time interval 404 may include, for example, events occurring during the first time interval 404, such as a charging session. Accordingly, a score may be assigned to each reliability indicator of the second time interval 405 by selecting data corresponding to the second time interval 405 from the historical information 501.

Based on the scores assigned to the reliability indicators 502, a first reliability index 503 may be calculated. If the reliability indicator 502 has been assigned a score for the first time interval 404 and the second time interval 405, respectively, a first reliability index 503 may be calculated based on the score assigned to the reliability indicator for the first time interval 404.

Based on the scores assigned to the reliability indicators 502, a second reliability index 504 may be calculated. If the reliability indicator 502 has been assigned a score for the first time interval and the second time interval, respectively, a second reliability index 504 may be calculated based on the scores assigned to the reliability indicator for the second time interval.

Based on the first reliability index 503 and the second reliability index 504, a third index 505 may be calculated.

Each reliability indicator may indicate how well or how poorly EV charging station 302 is operating. The maximum value of the scores assigned to all the reliability indicators may be 100, for example, and the minimum value of the scores may be 0. For example, 10 points may be used for assignment to reliability index 1, 50 points may be used for assignment to reliability index 2, and 40 points may be used for assignment to reliability index 3. The total number of reliability indicators used and the number of scores available for assignment may vary.

The individual reliability indicators may be direct reliability indicators or indirect reliability indicators.

Below, some reliability indicators are disclosed that may be used to assign scores to EV charging stations 302. These are merely example reliability indicators, and other reliability indicators may additionally or alternatively be used.

The at least one reliability indicator may include a percentage of offline time for the EV charging station.

The offline time percentage may refer to the percentage of EV charging stations that are offline during the relevant time interval. Herein, the relevant time interval may comprise, for example, the first time interval 404 or the second time interval 405. The total time that can be taken offline by taking the EV charging station 302 off-line during the relevant time intervalt_offlineDivided by the total length t of the relevant time interval_periodTo calculate the off-line time percentage p_{offline time}：

For example, if the length of the relevant time interval is t_period1440min and 24h, and during this time interval, the EV charging station 302 has gone offline for t_offlineAt 27min, the off-line time percentage would be 27/1440-1.9%.

For offline percentage of time p_{offline time}Assigned score value P_{offline time}This can be calculated, for example, using the following equation:

for example, in the case of an offline percentage of time of 1%, the score would have a value of 15 × (1-50 × 0.01) ═ 15 × 0.5 ═ 7.5.

Alternatively or additionally, the at least one reliability indicator may include a percentage of time to failure of the EV charging station.

The percentage of time to failure may refer to the percentage of EV charging stations 302 that are in a failed state during the relevant time interval. A failure may mean that the EV charging station 302 is online, but it has reported a failure and therefore the EV charging station 302 cannot be used. The total time t that can be taken to fail an EV charging station 302 during the relevant time interval_{fault time}Divided by the total length t of the relevant time interval_periodTo calculate the percentage of time to failure p_{fault time}：

For example, if the length of the relevant time interval is t_period1440min and at that time 24hDuring the interval, the EV charging station 302 has failed for t_offline29min, the off-line time percentage would be 29/1440-2.0%.

For percentage of time to failure p_{fault time}Assigned score value P_{fault time}This can be calculated, for example, using the following equation:

for example, in the case of a percentage of time to failure of 1%, the score would have a value of 15 × (1-50 × 0.01) ═ 15 × 0.5 ═ 7.5.

The offline time percentage and the fault time percentage may be referred to as direct reliability indicators, as these reliability indicators may directly indicate a fault in the EV charging station.

Alternatively or additionally, the at least one reliability indicator may include a number of warnings sent by the EV charging station.

The alert may refer to a different message sent by the EV charging station 302 to the backend system. The warning may indicate that something in the EV charging station 302 is unusual, but the station is still available for use. Typical warnings may include, for example, a weak wireless signal or excessive temperature. Number of warnings p_warningsCan be calculated as the average number of warnings per hour. For example, if a station sent 10 alerts during the last 24 hours, the average number of alerts would be p_warnings0.42 warnings/hour.

For example, the number of warnings p can be calculated using the following equation_warningsAssigned score value P_warnings：

For example, if the average number of warnings per hour is 0.28, the score value would be 10 × (0.43-0.28)/0.30 × (10 × 0.15/0.30 ═ 5.

Alternatively or additionally, the at least one reliability indicator may include a number of charges performed in less time than a preconfigured minimum time threshold using an EV charging station.

Short charging may refer to an event: charging begins but ends before it lasts longer than a preconfigured minimum time threshold.

The minimum time threshold may be in the range of 10 seconds to 5 minutes. The minimum time threshold may be within any subrange of this range, such as 10 seconds to 2 minutes, 1 minute to 3 minutes, or 5 seconds to 4 minutes. The minimum time threshold may be, for example, 30 seconds, 1 minute, 2 minutes, or 5 minutes.

A short charge may indicate that the EV cannot be charged with EV charging station 302 as planned. Short number of charges p_shortMay be calculated as the average short charge per hour. For example, if the EV charging station 302 had 1 short charge during the last 24 hours, the average would be 0.042 charges/hour.

For example, the number of short charges p can be calculated using the following equation_shortAssigned score value P_short：

For example, if the average number of short charges per hour is 0.10, the score value will be 15 × (0.16-0.10)/0.12 × (15 × 0.06/0.12 ═ 7.5.

Alternatively or additionally, the at least one reliability indicator may include a number of charges performed using the EV charging station with a charging energy less than a preconfigured energy threshold.

Charging with less energy than the preconfigured energy threshold generally indicates a situation: charging starts and may even last for several hours, but for some reason the EV does not receive a large amount of energy through this charging.

The preconfigured energy threshold may be in the range of 10 watt-hours (watt-hours) to 200 watt-hours, or any sub-range of this range, such as 20 watt-hours to 150 watt-hours, 50 watt-hours to 200 watt-hours, or 70 watt-hours to 130 watt-hours. The preconfigured energy threshold may be, for example, 100 watt-hours.

If the charging event is also short (see previous reliability indicators above), the charging energy is also typically less than the preconfigured energy threshold. In such a case, only short charging may be considered in the reliability indicator for that event, and the low energy reliability indicator may be ignored for that individual charging event. The number of charges having low energy may be calculated using the average number of low energy charges per hour. For example, if the charging station has 1 charge of less energy than the preconfigured energy threshold during the last 24 hours, the average would be 0.042 charges/hour.

For example, the number p of times of charging to low energy can be calculated using the following equation_energyAssigned score value P_energy：

For example, if the average number of low energy charges per hour is 0.10, the numerical value of the score will be 10 × (0.16-0.10)/0.12 × 10 × 0.06/0.12 ═ 5.

Alternatively or additionally, the at least one reliability indicator may include a number of charges performed using the EV charging station for longer than a preconfigured maximum time threshold.

The long charge may be a charge that lasts beyond a preconfigured maximum time threshold. The preconfigured maximum time threshold may be, for example, in the range of 6 hours (h) to 48h, or any sub-range of this range, such as 12h to 36h, 6h to 36h, or 18h to 32 h. The maximum time threshold may be 24h, for example. Also, a charge that has not yet ended but has exceeded a preconfigured maximum time threshold from the initial start may be considered a long charge.

A long charge may indicate that EV charging station 302 fails to report that charging has ended. In many cases, the EV has stopped charging a few hours ago, but the EV charging station 302 does not report the stop event to the backend system. Thus, the backend system is still under the impression that charging is in progress.

The long charge number may be calculated as an average long charge number per hour. For example, if the charging station has 5 long charges during the last 30 days, the average is about 0.0069 charges/hour.

For example, the number p of long charges can be calculated using the following equation_longAssigned score value P_long：

For example, if the average number of long charges per hour is 0.0067, the numerical value of the score will be 5 × (0.0092-0.0067)/0.005 ═ 5 × 0.0025/0.0050 × 2.5 minutes.

Alternatively or additionally, the at least one reliability indicator may include a number of failed charges performed using the EV charging station.

The failed charging may correspond to the following situation: the EV charging station 302 appears online and operating normally, and a user attempts to start charging using the EV charging station 302, but charging is not started. Typical reasons may be, for example: there are some technical problems with the EV charging station 302 and the station does not accept or process the start command sent to the EV charging station 302 at all.

Failed charging is generally an indicator that the EV charging station 302 has some unknown issues that the station does not normally report to the backend system in a normal fault/warning message. Failed charging may also be an indication of a telecommunications problem, such as a firewall blocking certain commands or the like.

The failed charge may be calculated as an average number of failed charges per hour. For example, if a station has 1 failed charge during the last 24 hours, the average number of failed charges would be 0.042 charges/hour.

For example, the number of failed charges p can be calculated using the following equation_failedAssigned score value P_failed：

For example, if the average number of warnings per hour is 0.12, the score value would be 10 × (0.20-0.12)/0.16 ═ 10 × 0.08/0.16 ═ 5.

Alternatively or additionally, the at least one reliability indicator may include a number of successful charges performed using the EV charging station.

Successful charging may be a positive indicator that many users are already able to charge through an EV charging station. Even if there is no problem (so the score from the previous index is full), it should not be assumed that the EV charging station 302 is operating fully normally. For example, there may be an obstacle such as a snow bank in front of the EV charging station 302, and the user therefore cannot use the EV charging station 302. The station itself may be operating properly, but the user still cannot use the EV charging station 302 to charge because the charging station is inaccessible. However, if one is always charging through the station, it can be assumed that the EV charging station 302 is operating normally without any problems.

Charging may be considered successful if charging has started and ended, the charging duration is between a preconfigured minimum time threshold and a preconfigured maximum time threshold, and the charged power exceeds a preconfigured energy threshold. The preconfigured minimum time threshold may comprise any of the values disclosed herein. The preconfigured maximum time threshold may comprise any of the values disclosed herein. The preconfigured energy threshold may comprise any of the values disclosed herein.

The number of successful charges may be calculated as the average number of successful charges per hour. For example, if there were 10 successful charges during 24h, then on average it would be about 0.42 successful charges per hour.

For example, the number of successful charges p can be calculated using the following equation_successAssigned score value P_success：

For example, if the average number of successful charges per hour is 0.105, the numerical value of the score will be 20 × p_success30 multiplied by 0.105/0.21 divided by 15.

The values disclosed in the above examples are merely exemplary and may be adjusted for different applications and situations.

The number of warnings sent by an EV charging station, the number of charges performed using an EV charging station in less than a preconfigured minimum time threshold, the number of charges performed using an EV charging station in less than a preconfigured energy threshold, the number of charges performed using an EV charging station in more than a preconfigured maximum time threshold, the number of failed charges performed using an EV charging station, and/or the number of successful charges performed using an EV charging station may be referred to as an indirect reliability indicator.

The first reliability index and/or the second reliability index may be calculated, for example, by summing all scores assigned to the at least one reliability indicator. If the total number of assigned scores is limited to 100 points, for example, the maximum value of the first/second reliability index is 100 points.

A reliability index such as the third reliability index 505 may be calibrated based on user feedback and experience with the EV charging station. Calibration may affect the scores that may be assigned for the various reliability indicators. Calibration may be done, for example, by collecting user feedback from different EV charging stations (helpdesk calls, support requests through web forms, social media messages, etc.). The feedback may be manually aggregated together (e.g., once a month), and based on the feedback, a user feedback index may be created. The user feedback index may indicate how much negative feedback the different EV charging stations receive from the user. The customer feedback index may then be compared to the reliability index to compare whether the results match.

As an example, if the worst performing EV charging station receives 50 negative customer feedbacks per month and the second to last performing EV charging station receives 40 negative feedbacks, then both EV charging stations should also receive a low reliability index. If this is not the case, the reliability index may be calibrated by testing different amounts of scores available for assignment to the individual reliability indicators and by checking if the reliability index better matches the user feedback after adjustment. This may be repeated in an iterative manner until the reliability index better matches the user feedback.

Any range or device value given herein may be extended or modified without losing the effect sought. Moreover, any embodiment may be combined with another embodiment unless explicitly prohibited.

Although the subject matter has been described in language specific to structural features and/or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as examples of implementing the claims, and other equivalent features and acts are intended to be within the scope of the claims.

It is to be understood that the benefits and advantages described above may relate to one embodiment or may relate to multiple embodiments. The embodiments are not limited to those embodiments that solve any or all of the stated problems or embodiments having any or all of the stated benefits and advantages. It should also be understood that reference to "an" item may refer to one or more of those items.

The steps of the methods described herein may be performed in any suitable order, or simultaneously where appropriate. Additionally, individual blocks may be deleted from any of the methods without departing from the spirit and scope of the subject matter described herein. Aspects of any of the above-described embodiments may be combined with aspects of any of the other described embodiments to form further embodiments without losing the effect sought.

As used herein, the term "comprising" is intended to include the identified method, block or element, but that such block or element does not include the exclusive list, and that the method or apparatus may include additional blocks or elements.

It is to be understood that the above description has been given by way of example only and that various modifications may be made by those skilled in the art. The above specification, examples and data provide a complete description of the structure and use of exemplary embodiments. Although various embodiments have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of this specification.

Claims

1. A method (100) comprising the steps of:

obtaining (101) historical information of an electric vehicle EV charging station by a computing device;

calculating (102), by the computing device, a first reliability index based on the historical information, wherein the first reliability index corresponds to the reliability of the EV charging station during a first time period;

calculating (103), by the computing device, a second reliability index based on the historical information, wherein the second reliability index corresponds to the reliability of the EV charging station during a second time period;

calculating (104), by the computing device, a third reliability index based on the first reliability index and the second reliability index; and

providing, by the computing device, the third reliability index to a user;

Wherein, the step of calculating the first reliability index or the second reliability index based on the historical information includes:

assigning a score to at least one reliability indicator based on the historical information; and

calculating the first reliability index or the second reliability index based on the assigned score;

Wherein, the at least one reliability index includes at least the number of times of charging performed using the EV charging station in a time shorter than a preconfigured minimum time threshold.

2. The method (100) of claim 1, wherein the length of the first time period is shorter than the length of the second time period.

3. The method (100) of claim 1 or claim 2, wherein the first time period corresponds to a time period between a current time and a first time threshold.

4. The method (100) of any preceding claim, wherein the second time period corresponds to a time period between the first and second time thresholds.

5. The method (100) of any preceding claim, wherein the length of the first period of time is in the range of 12 hours to 10 days.

6. The method (100) of any preceding claim, wherein the length of the second period of time is in the range of 30 days to 12 months.

7. The method (100) of any preceding claim, wherein the at least one reliability indicator further comprises at least one of the following:

the percentage of time that the EV charging station is offline;

the percentage of time that the EV charging station is down;

the number of warnings sent by said EV charging station;

the number of charges performed using the EV charging station for a time longer than a preconfigured maximum time threshold;

the number of times of charging performed using the EV charging station with a charging energy less than a preconfigured energy threshold;

the number of failed charges performed using the EV charging station; or

The number of successful charges performed using the EV charging station.

8. A computer program product comprising program code configured to perform the method of any of claims 1 to 7 when the computer program product is executed on a computer.

9. A computing device (200) configured to:

Obtain historical information of EV charging stations for electric vehicles (501);

calculating a first reliability index based on the historical information (503), wherein the first reliability index corresponds to the reliability of the EV charging station during a first time period;

calculating a second reliability index based on the historical information (504), wherein the second reliability index corresponds to the reliability of the EV charging station during a second time period;

calculating a third reliability index based on the first reliability index and the second reliability index (505); and

providing the third reliability index to the user;

Wherein, the computing device is configured to calculate the first reliability index or the second reliability index based on the historical information by performing the following operations:

assigning a score to at least one reliability indicator (503) based on the historical information; and

10. The computing device (200) of claim 9, wherein the length of the first time period is shorter than the length of the second time period.

11. The computing device (200) of claim 9 or claim 10, wherein the length of the first time period is in the range of 12 hours to 10 days.

12. The computing device (200) of any of claims 9 to 11, wherein the length of the second time period is in the range of 30 days to 12 months.

13. The computing device (200) according to any one of claims 9 to 12, wherein the at least one reliability indicator comprises at least one of the following: